Overview

Dataset statistics

Number of variables43
Number of observations125973
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.3 MiB
Average record size in memory344.0 B

Variable types

Numeric29
Categorical14

Alerts

num_outbound_cmds has constant value "0"Constant
service has a high cardinality: 70 distinct valuesHigh cardinality
src_bytes is highly overall correlated with dst_bytes and 10 other fieldsHigh correlation
dst_bytes is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
hot is highly overall correlated with num_compromised and 1 other fieldsHigh correlation
num_compromised is highly overall correlated with hotHigh correlation
count is highly overall correlated with src_bytes and 11 other fieldsHigh correlation
srv_count is highly overall correlated with count and 1 other fieldsHigh correlation
serror_rate is highly overall correlated with src_bytes and 9 other fieldsHigh correlation
srv_serror_rate is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
rerror_rate is highly overall correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly overall correlated with src_bytes and 13 other fieldsHigh correlation
diff_srv_rate is highly overall correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_count is highly overall correlated with count and 5 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
dst_host_same_srv_rate is highly overall correlated with src_bytes and 12 other fieldsHigh correlation
dst_host_diff_srv_rate is highly overall correlated with src_bytes and 6 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with src_bytes and 7 other fieldsHigh correlation
dst_host_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
protocol_type is highly overall correlated with srv_count and 2 other fieldsHigh correlation
service is highly overall correlated with protocol_type and 2 other fieldsHigh correlation
flag is highly overall correlated with logged_inHigh correlation
land is highly overall correlated with attack_categoryHigh correlation
wrong_fragment is highly overall correlated with attack_categoryHigh correlation
logged_in is highly overall correlated with count and 6 other fieldsHigh correlation
root_shell is highly overall correlated with su_attemptedHigh correlation
su_attempted is highly overall correlated with root_shellHigh correlation
is_guest_login is highly overall correlated with hot and 1 other fieldsHigh correlation
attack_category is highly overall correlated with protocol_type and 3 other fieldsHigh correlation
flag is highly imbalanced (55.8%)Imbalance
land is highly imbalanced (99.7%)Imbalance
wrong_fragment is highly imbalanced (95.1%)Imbalance
urgent is highly imbalanced (99.9%)Imbalance
root_shell is highly imbalanced (98.5%)Imbalance
su_attempted is highly imbalanced (99.5%)Imbalance
num_shells is highly imbalanced (99.7%)Imbalance
is_host_login is highly imbalanced (> 99.9%)Imbalance
is_guest_login is highly imbalanced (92.3%)Imbalance
attack_category is highly imbalanced (60.0%)Imbalance
src_bytes is highly skewed (γ1 = 190.669347)Skewed
dst_bytes is highly skewed (γ1 = 290.0529108)Skewed
num_failed_logins is highly skewed (γ1 = 53.7644243)Skewed
num_compromised is highly skewed (γ1 = 250.1078834)Skewed
num_root is highly skewed (γ1 = 236.913724)Skewed
num_file_creations is highly skewed (γ1 = 55.66534083)Skewed
num_access_files is highly skewed (γ1 = 45.55496112)Skewed
duration has 115955 (92.0%) zerosZeros
src_bytes has 49392 (39.2%) zerosZeros
dst_bytes has 67967 (54.0%) zerosZeros
hot has 123302 (97.9%) zerosZeros
num_failed_logins has 125851 (99.9%) zerosZeros
num_compromised has 124687 (99.0%) zerosZeros
num_root has 125324 (99.5%) zerosZeros
num_file_creations has 125686 (99.8%) zerosZeros
num_access_files has 125602 (99.7%) zerosZeros
serror_rate has 86829 (68.9%) zerosZeros
srv_serror_rate has 88754 (70.5%) zerosZeros
rerror_rate has 109783 (87.1%) zerosZeros
srv_rerror_rate has 109767 (87.1%) zerosZeros
same_srv_rate has 2766 (2.2%) zerosZeros
diff_srv_rate has 76217 (60.5%) zerosZeros
srv_diff_host_rate has 97574 (77.5%) zerosZeros
dst_host_same_srv_rate has 6927 (5.5%) zerosZeros
dst_host_diff_srv_rate has 46989 (37.3%) zerosZeros
dst_host_same_src_port_rate has 63023 (50.0%) zerosZeros
dst_host_srv_diff_host_rate has 86904 (69.0%) zerosZeros
dst_host_serror_rate has 81386 (64.6%) zerosZeros
dst_host_srv_serror_rate has 85360 (67.8%) zerosZeros
dst_host_rerror_rate has 103178 (81.9%) zerosZeros
dst_host_srv_rerror_rate has 106616 (84.6%) zerosZeros

Reproduction

Analysis started2023-04-15 19:01:45.359387
Analysis finished2023-04-15 19:06:32.908119
Duration4 minutes and 47.55 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

duration
Real number (ℝ)

Distinct2981
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.14465
Minimum0
Maximum42908
Zeros115955
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:33.263186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum42908
Range42908
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2604.5153
Coefficient of variation (CV)9.0703947
Kurtosis156.07681
Mean287.14465
Median Absolute Deviation (MAD)0
Skewness11.88023
Sum36172473
Variance6783500
MonotonicityNot monotonic
2023-04-16T00:06:33.829484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115955
92.0%
1 1989
 
1.6%
2 843
 
0.7%
3 557
 
0.4%
4 351
 
0.3%
5 298
 
0.2%
27 197
 
0.2%
6 193
 
0.2%
28 181
 
0.1%
7 127
 
0.1%
Other values (2971) 5282
 
4.2%
ValueCountFrequency (%)
0 115955
92.0%
1 1989
 
1.6%
2 843
 
0.7%
3 557
 
0.4%
4 351
 
0.3%
5 298
 
0.2%
6 193
 
0.2%
7 127
 
0.1%
8 98
 
0.1%
9 95
 
0.1%
ValueCountFrequency (%)
42908 1
< 0.1%
42888 1
< 0.1%
42862 1
< 0.1%
42837 1
< 0.1%
42804 1
< 0.1%
42778 1
< 0.1%
42746 1
< 0.1%
42723 1
< 0.1%
42699 1
< 0.1%
42679 1
< 0.1%

protocol_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
tcp
102689 
udp
14993 
icmp
 
8291

Length

Max length4
Median length3
Mean length3.0658157
Min length3

Characters and Unicode

Total characters386210
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowudp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 102689
81.5%
udp 14993
 
11.9%
icmp 8291
 
6.6%

Length

2023-04-16T00:06:34.225688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:34.643589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
tcp 102689
81.5%
udp 14993
 
11.9%
icmp 8291
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p 125973
32.6%
c 110980
28.7%
t 102689
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 386210
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 125973
32.6%
c 110980
28.7%
t 102689
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 386210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 125973
32.6%
c 110980
28.7%
t 102689
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 386210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 125973
32.6%
c 110980
28.7%
t 102689
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

service
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
http
40338 
private
21853 
domain_u
9043 
smtp
7313 
ftp_data
6860 
Other values (65)
40566 

Length

Max length11
Median length10
Mean length5.4664492
Min length3

Characters and Unicode

Total characters688625
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowftp_data
2nd rowother
3rd rowprivate
4th rowhttp
5th rowhttp

Common Values

ValueCountFrequency (%)
http 40338
32.0%
private 21853
17.3%
domain_u 9043
 
7.2%
smtp 7313
 
5.8%
ftp_data 6860
 
5.4%
eco_i 4586
 
3.6%
other 4359
 
3.5%
ecr_i 3077
 
2.4%
telnet 2353
 
1.9%
finger 1767
 
1.4%
Other values (60) 24424
19.4%

Length

2023-04-16T00:06:34.992671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http 40338
32.0%
private 21853
17.3%
domain_u 9043
 
7.2%
smtp 7313
 
5.8%
ftp_data 6860
 
5.4%
eco_i 4586
 
3.6%
other 4359
 
3.5%
ecr_i 3077
 
2.4%
telnet 2353
 
1.9%
finger 1767
 
1.4%
Other values (60) 24424
19.4%

Most occurring characters

ValueCountFrequency (%)
t 145597
21.1%
p 88151
12.8%
a 51384
 
7.5%
h 49666
 
7.2%
e 49119
 
7.1%
i 48525
 
7.0%
r 34885
 
5.1%
_ 29465
 
4.3%
o 24559
 
3.6%
n 22585
 
3.3%
Other values (30) 144689
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 651479
94.6%
Connector Punctuation 29465
 
4.3%
Decimal Number 6185
 
0.9%
Uppercase Letter 1496
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 145597
22.3%
p 88151
13.5%
a 51384
 
7.9%
h 49666
 
7.6%
e 49119
 
7.5%
i 48525
 
7.4%
r 34885
 
5.4%
o 24559
 
3.8%
n 22585
 
3.5%
v 22472
 
3.4%
Other values (15) 114536
17.6%
Decimal Number
ValueCountFrequency (%)
4 1708
27.6%
3 1656
26.8%
0 866
14.0%
5 862
13.9%
9 862
13.9%
1 148
 
2.4%
2 79
 
1.3%
8 3
 
< 0.1%
7 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 862
57.6%
C 187
 
12.5%
I 187
 
12.5%
R 187
 
12.5%
X 73
 
4.9%
Connector Punctuation
ValueCountFrequency (%)
_ 29465
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 652975
94.8%
Common 35650
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 145597
22.3%
p 88151
13.5%
a 51384
 
7.9%
h 49666
 
7.6%
e 49119
 
7.5%
i 48525
 
7.4%
r 34885
 
5.3%
o 24559
 
3.8%
n 22585
 
3.5%
v 22472
 
3.4%
Other values (20) 116032
17.8%
Common
ValueCountFrequency (%)
_ 29465
82.7%
4 1708
 
4.8%
3 1656
 
4.6%
0 866
 
2.4%
5 862
 
2.4%
9 862
 
2.4%
1 148
 
0.4%
2 79
 
0.2%
8 3
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 688625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 145597
21.1%
p 88151
12.8%
a 51384
 
7.5%
h 49666
 
7.2%
e 49119
 
7.1%
i 48525
 
7.0%
r 34885
 
5.1%
_ 29465
 
4.3%
o 24559
 
3.6%
n 22585
 
3.3%
Other values (30) 144689
21.0%

flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
SF
74945 
S0
34851 
REJ
11233 
RSTR
 
2421
RSTO
 
1562
Other values (6)
 
961

Length

Max length6
Median length2
Mean length2.1560414
Min length2

Characters and Unicode

Total characters271603
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowS0
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF 74945
59.5%
S0 34851
27.7%
REJ 11233
 
8.9%
RSTR 2421
 
1.9%
RSTO 1562
 
1.2%
S1 365
 
0.3%
SH 271
 
0.2%
S2 127
 
0.1%
RSTOS0 103
 
0.1%
S3 49
 
< 0.1%

Length

2023-04-16T00:06:35.451337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 74945
59.5%
s0 34851
27.7%
rej 11233
 
8.9%
rstr 2421
 
1.9%
rsto 1562
 
1.2%
s1 365
 
0.3%
sh 271
 
0.2%
s2 127
 
0.1%
rstos0 103
 
0.1%
s3 49
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 114797
42.3%
F 74945
27.6%
0 34954
 
12.9%
R 17740
 
6.5%
E 11233
 
4.1%
J 11233
 
4.1%
T 4132
 
1.5%
O 1711
 
0.6%
1 365
 
0.1%
H 317
 
0.1%
Other values (2) 176
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 236108
86.9%
Decimal Number 35495
 
13.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 114797
48.6%
F 74945
31.7%
R 17740
 
7.5%
E 11233
 
4.8%
J 11233
 
4.8%
T 4132
 
1.8%
O 1711
 
0.7%
H 317
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 34954
98.5%
1 365
 
1.0%
2 127
 
0.4%
3 49
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 236108
86.9%
Common 35495
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 114797
48.6%
F 74945
31.7%
R 17740
 
7.5%
E 11233
 
4.8%
J 11233
 
4.8%
T 4132
 
1.8%
O 1711
 
0.7%
H 317
 
0.1%
Common
ValueCountFrequency (%)
0 34954
98.5%
1 365
 
1.0%
2 127
 
0.4%
3 49
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 114797
42.3%
F 74945
27.6%
0 34954
 
12.9%
R 17740
 
6.5%
E 11233
 
4.1%
J 11233
 
4.1%
T 4132
 
1.5%
O 1711
 
0.6%
1 365
 
0.1%
H 317
 
0.1%
Other values (2) 176
 
0.1%

src_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3341
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45566.743
Minimum0
Maximum1.3799639 × 109
Zeros49392
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:35.995882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3276
95-th percentile1480
Maximum1.3799639 × 109
Range1.3799639 × 109
Interquartile range (IQR)276

Descriptive statistics

Standard deviation5870331.2
Coefficient of variation (CV)128.82929
Kurtosis39354.121
Mean45566.743
Median Absolute Deviation (MAD)44
Skewness190.66935
Sum5.7401793 × 109
Variance3.4460788 × 1013
MonotonicityNot monotonic
2023-04-16T00:06:36.615466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49392
39.2%
8 3691
 
2.9%
1 2432
 
1.9%
44 2334
 
1.9%
45 2089
 
1.7%
1032 2001
 
1.6%
46 1294
 
1.0%
43 1284
 
1.0%
105 998
 
0.8%
147 948
 
0.8%
Other values (3331) 59510
47.2%
ValueCountFrequency (%)
0 49392
39.2%
1 2432
 
1.9%
4 2
 
< 0.1%
5 28
 
< 0.1%
6 147
 
0.1%
7 107
 
0.1%
8 3691
 
2.9%
9 199
 
0.2%
10 195
 
0.2%
11 76
 
0.1%
ValueCountFrequency (%)
1379963888 1
< 0.1%
1167519497 1
< 0.1%
693375640 1
< 0.1%
621568663 1
< 0.1%
381709090 1
< 0.1%
217277339 1
< 0.1%
89581520 1
< 0.1%
24418776 1
< 0.1%
21945520 1
< 0.1%
18828976 1
< 0.1%

dst_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9326
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19779.114
Minimum0
Maximum1.3099374 × 109
Zeros67967
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:37.177534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3516
95-th percentile8314
Maximum1.3099374 × 109
Range1.3099374 × 109
Interquartile range (IQR)516

Descriptive statistics

Standard deviation4021269.2
Coefficient of variation (CV)203.30886
Kurtosis90941.735
Mean19779.114
Median Absolute Deviation (MAD)0
Skewness290.05291
Sum2.4916344 × 109
Variance1.6170606 × 1013
MonotonicityNot monotonic
2023-04-16T00:06:37.701615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67967
54.0%
105 1497
 
1.2%
8314 888
 
0.7%
330 528
 
0.4%
331 512
 
0.4%
44 511
 
0.4%
42 478
 
0.4%
328 470
 
0.4%
332 469
 
0.4%
4 454
 
0.4%
Other values (9316) 52199
41.4%
ValueCountFrequency (%)
0 67967
54.0%
1 22
 
< 0.1%
3 1
 
< 0.1%
4 454
 
0.4%
5 4
 
< 0.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 47
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
1309937401 1
< 0.1%
400291060 2
< 0.1%
7028652 1
< 0.1%
5155468 1
< 0.1%
5153771 1
< 0.1%
5153460 1
< 0.1%
5151385 1
< 0.1%
5151154 1
< 0.1%
5151049 1
< 0.1%
5150938 1
< 0.1%

land
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125948 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125948
> 99.9%
1 25
 
< 0.1%

Length

2023-04-16T00:06:38.260873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:38.674987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125948
> 99.9%
1 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125948
> 99.9%
1 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125948
> 99.9%
1 25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125948
> 99.9%
1 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125948
> 99.9%
1 25
 
< 0.1%

wrong_fragment
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
124883 
3
 
884
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124883
99.1%
3 884
 
0.7%
1 206
 
0.2%

Length

2023-04-16T00:06:39.008893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:39.413725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 124883
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 124883
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124883
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124883
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124883
99.1%
3 884
 
0.7%
1 206
 
0.2%

urgent
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125964 
1
 
5
2
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125964
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Length

2023-04-16T00:06:39.745044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:40.167094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125964
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125964
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125964
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125964
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125964
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

hot
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20440888
Minimum0
Maximum77
Zeros123302
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:40.439581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1499684
Coefficient of variation (CV)10.51798
Kurtosis168.01426
Mean0.20440888
Median Absolute Deviation (MAD)0
Skewness12.589886
Sum25750
Variance4.6223643
MonotonicityNot monotonic
2023-04-16T00:06:40.764339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123302
97.9%
2 1037
 
0.8%
1 369
 
0.3%
28 277
 
0.2%
30 256
 
0.2%
4 173
 
0.1%
6 140
 
0.1%
5 76
 
0.1%
24 68
 
0.1%
19 57
 
< 0.1%
Other values (18) 218
 
0.2%
ValueCountFrequency (%)
0 123302
97.9%
1 369
 
0.3%
2 1037
 
0.8%
3 54
 
< 0.1%
4 173
 
0.1%
5 76
 
0.1%
6 140
 
0.1%
7 5
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
77 1
 
< 0.1%
44 2
 
< 0.1%
33 1
 
< 0.1%
30 256
0.2%
28 277
0.2%
25 2
 
< 0.1%
24 68
 
0.1%
22 55
 
< 0.1%
21 1
 
< 0.1%
20 9
 
< 0.1%

num_failed_logins
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0012224842
Minimum0
Maximum5
Zeros125851
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:41.018641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.045239139
Coefficient of variation (CV)37.005909
Kurtosis3869.0693
Mean0.0012224842
Median Absolute Deviation (MAD)0
Skewness53.764424
Sum154
Variance0.0020465797
MonotonicityNot monotonic
2023-04-16T00:06:41.346582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 125851
99.9%
1 104
 
0.1%
2 9
 
< 0.1%
3 5
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 125851
99.9%
1 104
 
0.1%
2 9
 
< 0.1%
3 5
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 3
 
< 0.1%
3 5
 
< 0.1%
2 9
 
< 0.1%
1 104
 
0.1%
0 125851
99.9%

logged_in
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
76121 
1
49852 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 76121
60.4%
1 49852
39.6%

Length

2023-04-16T00:06:41.771303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:42.107530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 76121
60.4%
1 49852
39.6%

Most occurring characters

ValueCountFrequency (%)
0 76121
60.4%
1 49852
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76121
60.4%
1 49852
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76121
60.4%
1 49852
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76121
60.4%
1 49852
39.6%

num_compromised
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27925032
Minimum0
Maximum7479
Zeros124687
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:42.344666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7479
Range7479
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.942042
Coefficient of variation (CV)85.736849
Kurtosis75956.228
Mean0.27925032
Median Absolute Deviation (MAD)0
Skewness250.10788
Sum35178
Variance573.22139
MonotonicityNot monotonic
2023-04-16T00:06:42.742298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124687
99.0%
1 976
 
0.8%
2 98
 
0.1%
4 40
 
< 0.1%
3 38
 
< 0.1%
6 19
 
< 0.1%
5 17
 
< 0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
Other values (78) 87
 
0.1%
ValueCountFrequency (%)
0 124687
99.0%
1 976
 
0.8%
2 98
 
0.1%
3 38
 
< 0.1%
4 40
 
< 0.1%
5 17
 
< 0.1%
6 19
 
< 0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
7479 1
< 0.1%
1739 1
< 0.1%
1043 1
< 0.1%
884 2
< 0.1%
809 1
< 0.1%
789 1
< 0.1%
767 1
< 0.1%
761 1
< 0.1%
756 1
< 0.1%
751 1
< 0.1%

root_shell
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125804 
1
 
169

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125804
99.9%
1 169
 
0.1%

Length

2023-04-16T00:06:43.090826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:43.402855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125804
99.9%
1 169
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 125804
99.9%
1 169
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125804
99.9%
1 169
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125804
99.9%
1 169
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125804
99.9%
1 169
 
0.1%

su_attempted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125893 
2
 
59
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125893
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Length

2023-04-16T00:06:43.766229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:44.105143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125893
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125893
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125893
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125893
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125893
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

num_root
Real number (ℝ)

SKEWED  ZEROS 

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30219174
Minimum0
Maximum7468
Zeros125324
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:44.372059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7468
Range7468
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.399618
Coefficient of variation (CV)80.742174
Kurtosis70070.209
Mean0.30219174
Median Absolute Deviation (MAD)0
Skewness236.91372
Sum38068
Variance595.34136
MonotonicityNot monotonic
2023-04-16T00:06:44.746225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125324
99.5%
1 273
 
0.2%
9 121
 
0.1%
6 99
 
0.1%
2 33
 
< 0.1%
5 24
 
< 0.1%
4 12
 
< 0.1%
3 7
 
< 0.1%
7 2
 
< 0.1%
857 2
 
< 0.1%
Other values (72) 76
 
0.1%
ValueCountFrequency (%)
0 125324
99.5%
1 273
 
0.2%
2 33
 
< 0.1%
3 7
 
< 0.1%
4 12
 
< 0.1%
5 24
 
< 0.1%
6 99
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 121
 
0.1%
ValueCountFrequency (%)
7468 1
< 0.1%
1743 1
< 0.1%
1045 1
< 0.1%
993 1
< 0.1%
975 1
< 0.1%
889 1
< 0.1%
867 1
< 0.1%
857 2
< 0.1%
849 1
< 0.1%
841 1
< 0.1%

num_file_creations
Real number (ℝ)

SKEWED  ZEROS 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012669382
Minimum0
Maximum43
Zeros125686
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:45.088940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48393507
Coefficient of variation (CV)38.197213
Kurtosis3603.3117
Mean0.012669382
Median Absolute Deviation (MAD)0
Skewness55.665341
Sum1596
Variance0.23419315
MonotonicityNot monotonic
2023-04-16T00:06:45.418027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 125686
99.8%
1 151
 
0.1%
2 41
 
< 0.1%
4 13
 
< 0.1%
3 5
 
< 0.1%
8 5
 
< 0.1%
15 5
 
< 0.1%
10 5
 
< 0.1%
5 5
 
< 0.1%
17 5
 
< 0.1%
Other values (25) 52
 
< 0.1%
ValueCountFrequency (%)
0 125686
99.8%
1 151
 
0.1%
2 41
 
< 0.1%
3 5
 
< 0.1%
4 13
 
< 0.1%
5 5
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
43 1
 
< 0.1%
40 3
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
< 0.1%

num_shells
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125926 
1
 
42
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125926
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Length

2023-04-16T00:06:46.912959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:47.284544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125926
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125926
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125926
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125926
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125926
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

num_access_files
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0040961158
Minimum0
Maximum9
Zeros125602
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:47.555618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.099369556
Coefficient of variation (CV)24.259459
Kurtosis2862.8044
Mean0.0040961158
Median Absolute Deviation (MAD)0
Skewness45.554961
Sum516
Variance0.0098743086
MonotonicityNot monotonic
2023-04-16T00:06:47.826726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 125602
99.7%
1 313
 
0.2%
2 29
 
< 0.1%
3 8
 
< 0.1%
5 6
 
< 0.1%
4 5
 
< 0.1%
6 4
 
< 0.1%
8 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 125602
99.7%
1 313
 
0.2%
2 29
 
< 0.1%
3 8
 
< 0.1%
4 5
 
< 0.1%
5 6
 
< 0.1%
6 4
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 3
 
< 0.1%
7 2
 
< 0.1%
6 4
 
< 0.1%
5 6
 
< 0.1%
4 5
 
< 0.1%
3 8
 
< 0.1%
2 29
 
< 0.1%
1 313
 
0.2%
0 125602
99.7%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125973 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125973
100.0%

Length

2023-04-16T00:06:48.192981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:48.579738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125973
100.0%

Most occurring characters

ValueCountFrequency (%)
0 125973
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125973
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125973
100.0%

is_host_login
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125972 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125972
> 99.9%
1 1
 
< 0.1%

Length

2023-04-16T00:06:48.811653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:49.186077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125972
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125972
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125972
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125972
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125972
> 99.9%
1 1
 
< 0.1%

is_guest_login
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
124786 
1
 
1187

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124786
99.1%
1 1187
 
0.9%

Length

2023-04-16T00:06:49.428922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-16T00:06:49.747254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 124786
99.1%
1 1187
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 124786
99.1%
1 1187
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124786
99.1%
1 1187
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124786
99.1%
1 1187
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124786
99.1%
1 1187
 
0.9%

count
Real number (ℝ)

Distinct512
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.107555
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:50.022420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median14
Q3143
95-th percentile286
Maximum511
Range511
Interquartile range (IQR)141

Descriptive statistics

Standard deviation114.50861
Coefficient of variation (CV)1.3614545
Kurtosis2.0069166
Mean84.107555
Median Absolute Deviation (MAD)13
Skewness1.5142745
Sum10595281
Variance13112.221
MonotonicityNot monotonic
2023-04-16T00:06:50.493318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27763
22.0%
2 9474
 
7.5%
3 3962
 
3.1%
4 3550
 
2.8%
5 2980
 
2.4%
6 2413
 
1.9%
7 2325
 
1.8%
8 1902
 
1.5%
9 1712
 
1.4%
10 1610
 
1.3%
Other values (502) 68282
54.2%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 27763
22.0%
2 9474
 
7.5%
3 3962
 
3.1%
4 3550
 
2.8%
5 2980
 
2.4%
6 2413
 
1.9%
7 2325
 
1.8%
8 1902
 
1.5%
9 1712
 
1.4%
ValueCountFrequency (%)
511 1437
1.1%
510 307
 
0.2%
509 243
 
0.2%
508 31
 
< 0.1%
507 6
 
< 0.1%
506 3
 
< 0.1%
505 2
 
< 0.1%
504 3
 
< 0.1%
503 4
 
< 0.1%
502 5
 
< 0.1%

srv_count
Real number (ℝ)

Distinct509
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.737888
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:50.845699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8
Q318
95-th percentile158
Maximum511
Range511
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.63584
Coefficient of variation (CV)2.6186507
Kurtosis24.244484
Mean27.737888
Median Absolute Deviation (MAD)7
Skewness4.6941619
Sum3494225
Variance5275.9652
MonotonicityNot monotonic
2023-04-16T00:06:51.296050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25398
20.2%
2 12820
 
10.2%
3 6336
 
5.0%
4 5526
 
4.4%
5 4636
 
3.7%
6 4156
 
3.3%
7 3992
 
3.2%
8 3697
 
2.9%
9 3528
 
2.8%
11 3293
 
2.6%
Other values (499) 52591
41.7%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 25398
20.2%
2 12820
10.2%
3 6336
 
5.0%
4 5526
 
4.4%
5 4636
 
3.7%
6 4156
 
3.3%
7 3992
 
3.2%
8 3697
 
2.9%
9 3528
 
2.8%
ValueCountFrequency (%)
511 1012
0.8%
510 160
 
0.1%
509 49
 
< 0.1%
508 11
 
< 0.1%
507 3
 
< 0.1%
503 1
 
< 0.1%
502 2
 
< 0.1%
501 1
 
< 0.1%
500 2
 
< 0.1%
499 2
 
< 0.1%

serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28448453
Minimum0
Maximum1
Zeros86829
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:51.641735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44645562
Coefficient of variation (CV)1.5693494
Kurtosis-1.0546041
Mean0.28448453
Median Absolute Deviation (MAD)0
Skewness0.96320051
Sum35837.37
Variance0.19932262
MonotonicityNot monotonic
2023-04-16T00:06:52.041081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86829
68.9%
1 34439
 
27.3%
0.5 493
 
0.4%
0.33 321
 
0.3%
0.07 305
 
0.2%
0.06 298
 
0.2%
0.08 254
 
0.2%
0.99 250
 
0.2%
0.01 216
 
0.2%
0.25 208
 
0.2%
Other values (79) 2360
 
1.9%
ValueCountFrequency (%)
0 86829
68.9%
0.01 216
 
0.2%
0.02 84
 
0.1%
0.03 150
 
0.1%
0.04 131
 
0.1%
0.05 192
 
0.2%
0.06 298
 
0.2%
0.07 305
 
0.2%
0.08 254
 
0.2%
0.09 189
 
0.2%
ValueCountFrequency (%)
1 34439
27.3%
0.99 250
 
0.2%
0.98 64
 
0.1%
0.97 79
 
0.1%
0.96 41
 
< 0.1%
0.95 29
 
< 0.1%
0.94 25
 
< 0.1%
0.93 18
 
< 0.1%
0.92 15
 
< 0.1%
0.91 8
 
< 0.1%

srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28248537
Minimum0
Maximum1
Zeros88754
Zeros (%)70.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:52.491474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4470225
Coefficient of variation (CV)1.5824625
Kurtosis-1.0442938
Mean0.28248537
Median Absolute Deviation (MAD)0
Skewness0.97059724
Sum35585.53
Variance0.19982911
MonotonicityNot monotonic
2023-04-16T00:06:52.889717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88754
70.5%
1 34874
 
27.7%
0.5 432
 
0.3%
0.33 273
 
0.2%
0.25 233
 
0.2%
0.2 132
 
0.1%
0.17 114
 
0.1%
0.05 93
 
0.1%
0.03 92
 
0.1%
0.04 83
 
0.1%
Other values (76) 893
 
0.7%
ValueCountFrequency (%)
0 88754
70.5%
0.01 6
 
< 0.1%
0.02 60
 
< 0.1%
0.03 92
 
0.1%
0.04 83
 
0.1%
0.05 93
 
0.1%
0.06 65
 
0.1%
0.07 67
 
0.1%
0.08 63
 
0.1%
0.09 44
 
< 0.1%
ValueCountFrequency (%)
1 34874
27.7%
0.96 1
 
< 0.1%
0.95 40
 
< 0.1%
0.94 13
 
< 0.1%
0.93 8
 
< 0.1%
0.92 12
 
< 0.1%
0.91 16
 
< 0.1%
0.9 10
 
< 0.1%
0.89 12
 
< 0.1%
0.88 11
 
< 0.1%

rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11995848
Minimum0
Maximum1
Zeros109783
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:53.293105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32043552
Coefficient of variation (CV)2.6712202
Kurtosis3.4458471
Mean0.11995848
Median Absolute Deviation (MAD)0
Skewness2.3255316
Sum15111.53
Variance0.10267892
MonotonicityNot monotonic
2023-04-16T00:06:53.647254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109783
87.1%
1 12874
 
10.2%
0.9 269
 
0.2%
0.92 216
 
0.2%
0.93 210
 
0.2%
0.89 196
 
0.2%
0.91 187
 
0.1%
0.5 163
 
0.1%
0.88 141
 
0.1%
0.95 137
 
0.1%
Other values (72) 1797
 
1.4%
ValueCountFrequency (%)
0 109783
87.1%
0.01 61
 
< 0.1%
0.02 77
 
0.1%
0.03 99
 
0.1%
0.04 55
 
< 0.1%
0.05 37
 
< 0.1%
0.06 25
 
< 0.1%
0.07 23
 
< 0.1%
0.08 23
 
< 0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 12874
10.2%
0.99 23
 
< 0.1%
0.98 17
 
< 0.1%
0.97 32
 
< 0.1%
0.96 72
 
0.1%
0.95 137
 
0.1%
0.94 121
 
0.1%
0.93 210
 
0.2%
0.92 216
 
0.2%
0.91 187
 
0.1%

srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12118327
Minimum0
Maximum1
Zeros109767
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:54.091559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32364723
Coefficient of variation (CV)2.6707253
Kurtosis3.4458342
Mean0.12118327
Median Absolute Deviation (MAD)0
Skewness2.3270328
Sum15265.82
Variance0.10474753
MonotonicityNot monotonic
2023-04-16T00:06:54.657763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109767
87.1%
1 14827
 
11.8%
0.5 244
 
0.2%
0.33 160
 
0.1%
0.25 114
 
0.1%
0.2 92
 
0.1%
0.17 73
 
0.1%
0.04 50
 
< 0.1%
0.03 47
 
< 0.1%
0.14 45
 
< 0.1%
Other values (52) 554
 
0.4%
ValueCountFrequency (%)
0 109767
87.1%
0.01 3
 
< 0.1%
0.02 40
 
< 0.1%
0.03 47
 
< 0.1%
0.04 50
 
< 0.1%
0.05 42
 
< 0.1%
0.06 33
 
< 0.1%
0.07 27
 
< 0.1%
0.08 40
 
< 0.1%
0.09 24
 
< 0.1%
ValueCountFrequency (%)
1 14827
11.8%
0.96 2
 
< 0.1%
0.95 1
 
< 0.1%
0.92 2
 
< 0.1%
0.9 1
 
< 0.1%
0.89 3
 
< 0.1%
0.88 4
 
< 0.1%
0.87 3
 
< 0.1%
0.86 5
 
< 0.1%
0.85 10
 
< 0.1%

same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66092766
Minimum0
Maximum1
Zeros2766
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:55.120867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.43962286
Coefficient of variation (CV)0.66516033
Kurtosis-1.6097658
Mean0.66092766
Median Absolute Deviation (MAD)0
Skewness-0.57249948
Sum83259.04
Variance0.19326826
MonotonicityNot monotonic
2023-04-16T00:06:55.513009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 76812
61.0%
0.01 4027
 
3.2%
0.02 3616
 
2.9%
0.03 3503
 
2.8%
0.07 3438
 
2.7%
0.04 3227
 
2.6%
0.06 3220
 
2.6%
0.05 3088
 
2.5%
0.08 2815
 
2.2%
0 2766
 
2.2%
Other values (91) 19461
 
15.4%
ValueCountFrequency (%)
0 2766
2.2%
0.01 4027
3.2%
0.02 3616
2.9%
0.03 3503
2.8%
0.04 3227
2.6%
0.05 3088
2.5%
0.06 3220
2.6%
0.07 3438
2.7%
0.08 2815
2.2%
0.09 1957
1.6%
ValueCountFrequency (%)
1 76812
61.0%
0.99 759
 
0.6%
0.98 97
 
0.1%
0.97 44
 
< 0.1%
0.96 16
 
< 0.1%
0.95 14
 
< 0.1%
0.94 21
 
< 0.1%
0.93 33
 
< 0.1%
0.92 43
 
< 0.1%
0.91 25
 
< 0.1%

diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063052638
Minimum0
Maximum1
Zeros76217
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:55.962727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.18031441
Coefficient of variation (CV)2.8597441
Kurtosis18.899472
Mean0.063052638
Median Absolute Deviation (MAD)0
Skewness4.3798154
Sum7942.93
Variance0.032513286
MonotonicityNot monotonic
2023-04-16T00:06:56.393680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76217
60.5%
0.06 18998
 
15.1%
0.07 9515
 
7.6%
0.05 6887
 
5.5%
1 3438
 
2.7%
0.08 1883
 
1.5%
0.01 1013
 
0.8%
0.09 645
 
0.5%
0.04 627
 
0.5%
0.5 549
 
0.4%
Other values (85) 6201
 
4.9%
ValueCountFrequency (%)
0 76217
60.5%
0.01 1013
 
0.8%
0.02 264
 
0.2%
0.03 282
 
0.2%
0.04 627
 
0.5%
0.05 6887
 
5.5%
0.06 18998
 
15.1%
0.07 9515
 
7.6%
0.08 1883
 
1.5%
0.09 645
 
0.5%
ValueCountFrequency (%)
1 3438
2.7%
0.99 39
 
< 0.1%
0.98 6
 
< 0.1%
0.97 7
 
< 0.1%
0.96 29
 
< 0.1%
0.95 39
 
< 0.1%
0.92 2
 
< 0.1%
0.91 1
 
< 0.1%
0.9 1
 
< 0.1%
0.89 1
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097321648
Minimum0
Maximum1
Zeros97574
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:56.726003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2598305
Coefficient of variation (CV)2.6698119
Kurtosis6.8163072
Mean0.097321648
Median Absolute Deviation (MAD)0
Skewness2.8603545
Sum12259.9
Variance0.067511888
MonotonicityNot monotonic
2023-04-16T00:06:57.014482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97574
77.5%
1 8143
 
6.5%
0.01 2865
 
2.3%
0.5 982
 
0.8%
0.67 975
 
0.8%
0.12 904
 
0.7%
0.33 790
 
0.6%
0.02 771
 
0.6%
0.11 732
 
0.6%
0.25 724
 
0.6%
Other values (50) 11513
 
9.1%
ValueCountFrequency (%)
0 97574
77.5%
0.01 2865
 
2.3%
0.02 771
 
0.6%
0.03 218
 
0.2%
0.04 187
 
0.1%
0.05 325
 
0.3%
0.06 520
 
0.4%
0.07 519
 
0.4%
0.08 653
 
0.5%
0.09 618
 
0.5%
ValueCountFrequency (%)
1 8143
6.5%
0.88 1
 
< 0.1%
0.83 7
 
< 0.1%
0.8 60
 
< 0.1%
0.75 235
 
0.2%
0.71 9
 
< 0.1%
0.67 975
 
0.8%
0.62 7
 
< 0.1%
0.6 178
 
0.1%
0.57 33
 
< 0.1%

dst_host_count
Real number (ℝ)

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.14894
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:57.323639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q182
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)173

Descriptive statistics

Standard deviation99.206213
Coefficient of variation (CV)0.54464336
Kurtosis-1.0657728
Mean182.14894
Median Absolute Deviation (MAD)0
Skewness-0.83343768
Sum22945849
Variance9841.8727
MonotonicityNot monotonic
2023-04-16T00:06:57.722435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 74099
58.8%
1 3119
 
2.5%
2 2733
 
2.2%
3 1280
 
1.0%
4 1198
 
1.0%
5 723
 
0.6%
6 701
 
0.6%
7 645
 
0.5%
8 595
 
0.5%
9 578
 
0.5%
Other values (246) 40302
32.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 3119
2.5%
2 2733
2.2%
3 1280
1.0%
4 1198
 
1.0%
5 723
 
0.6%
6 701
 
0.6%
7 645
 
0.5%
8 595
 
0.5%
9 578
 
0.5%
ValueCountFrequency (%)
255 74099
58.8%
254 70
 
0.1%
253 89
 
0.1%
252 77
 
0.1%
251 90
 
0.1%
250 93
 
0.1%
249 78
 
0.1%
248 87
 
0.1%
247 89
 
0.1%
246 83
 
0.1%

dst_host_srv_count
Real number (ℝ)

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.65301
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:58.078739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median63
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)245

Descriptive statistics

Standard deviation110.70274
Coefficient of variation (CV)0.95719727
Kurtosis-1.7563349
Mean115.65301
Median Absolute Deviation (MAD)61
Skewness0.28372119
Sum14569156
Variance12255.097
MonotonicityNot monotonic
2023-04-16T00:06:58.357430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 35993
28.6%
1 8449
 
6.7%
2 5161
 
4.1%
3 2768
 
2.2%
4 2488
 
2.0%
5 2336
 
1.9%
20 2300
 
1.8%
254 2238
 
1.8%
6 2222
 
1.8%
19 2190
 
1.7%
Other values (246) 59828
47.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 8449
6.7%
2 5161
4.1%
3 2768
 
2.2%
4 2488
 
2.0%
5 2336
 
1.9%
6 2222
 
1.8%
7 2160
 
1.7%
8 2072
 
1.6%
9 1948
 
1.5%
ValueCountFrequency (%)
255 35993
28.6%
254 2238
 
1.8%
253 472
 
0.4%
252 213
 
0.2%
251 402
 
0.3%
250 302
 
0.2%
249 248
 
0.2%
248 205
 
0.2%
247 220
 
0.2%
246 196
 
0.2%

dst_host_same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52124169
Minimum0
Maximum1
Zeros6927
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:58.634173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.51
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.44894936
Coefficient of variation (CV)0.86130747
Kurtosis-1.8840459
Mean0.52124169
Median Absolute Deviation (MAD)0.49
Skewness-0.010448021
Sum65662.38
Variance0.20155553
MonotonicityNot monotonic
2023-04-16T00:06:58.901306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 49059
38.9%
0.01 7780
 
6.2%
0 6927
 
5.5%
0.02 6593
 
5.2%
0.07 5672
 
4.5%
0.04 5208
 
4.1%
0.05 4951
 
3.9%
0.03 4049
 
3.2%
0.06 3444
 
2.7%
0.08 2816
 
2.2%
Other values (91) 29474
23.4%
ValueCountFrequency (%)
0 6927
5.5%
0.01 7780
6.2%
0.02 6593
5.2%
0.03 4049
3.2%
0.04 5208
4.1%
0.05 4951
3.9%
0.06 3444
2.7%
0.07 5672
4.5%
0.08 2816
 
2.2%
0.09 1740
 
1.4%
ValueCountFrequency (%)
1 49059
38.9%
0.99 688
 
0.5%
0.98 821
 
0.7%
0.97 478
 
0.4%
0.96 675
 
0.5%
0.95 580
 
0.5%
0.94 393
 
0.3%
0.93 457
 
0.4%
0.92 341
 
0.3%
0.91 395
 
0.3%

dst_host_diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082951109
Minimum0
Maximum1
Zeros46989
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:59.188442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.07
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.1889218
Coefficient of variation (CV)2.2775078
Kurtosis12.63441
Mean0.082951109
Median Absolute Deviation (MAD)0.02
Skewness3.6096004
Sum10449.6
Variance0.035691446
MonotonicityNot monotonic
2023-04-16T00:06:59.440726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46989
37.3%
0.07 16570
 
13.2%
0.06 9787
 
7.8%
0.01 9295
 
7.4%
0.05 7321
 
5.8%
0.08 7001
 
5.6%
0.02 6716
 
5.3%
0.03 3563
 
2.8%
0.04 3091
 
2.5%
0.09 2569
 
2.0%
Other values (91) 13071
 
10.4%
ValueCountFrequency (%)
0 46989
37.3%
0.01 9295
 
7.4%
0.02 6716
 
5.3%
0.03 3563
 
2.8%
0.04 3091
 
2.5%
0.05 7321
 
5.8%
0.06 9787
 
7.8%
0.07 16570
 
13.2%
0.08 7001
 
5.6%
0.09 2569
 
2.0%
ValueCountFrequency (%)
1 2139
1.7%
0.99 31
 
< 0.1%
0.98 35
 
< 0.1%
0.97 86
 
0.1%
0.96 63
 
0.1%
0.95 87
 
0.1%
0.94 45
 
< 0.1%
0.93 54
 
< 0.1%
0.92 40
 
< 0.1%
0.91 86
 
0.1%

dst_host_same_src_port_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14837886
Minimum0
Maximum1
Zeros63023
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:06:59.677272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.30899713
Coefficient of variation (CV)2.0824876
Kurtosis2.7624021
Mean0.14837886
Median Absolute Deviation (MAD)0
Skewness2.0870394
Sum18691.73
Variance0.095479227
MonotonicityNot monotonic
2023-04-16T00:06:59.913049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63023
50.0%
0.01 17657
 
14.0%
1 10307
 
8.2%
0.02 5743
 
4.6%
0.03 3278
 
2.6%
0.04 2096
 
1.7%
0.05 1664
 
1.3%
0.06 1299
 
1.0%
0.08 1086
 
0.9%
0.5 1077
 
0.9%
Other values (91) 18743
 
14.9%
ValueCountFrequency (%)
0 63023
50.0%
0.01 17657
 
14.0%
0.02 5743
 
4.6%
0.03 3278
 
2.6%
0.04 2096
 
1.7%
0.05 1664
 
1.3%
0.06 1299
 
1.0%
0.07 1051
 
0.8%
0.08 1086
 
0.9%
0.09 712
 
0.6%
ValueCountFrequency (%)
1 10307
8.2%
0.99 139
 
0.1%
0.98 192
 
0.2%
0.97 145
 
0.1%
0.96 229
 
0.2%
0.95 220
 
0.2%
0.94 113
 
0.1%
0.93 159
 
0.1%
0.92 124
 
0.1%
0.91 149
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03254245
Minimum0
Maximum1
Zeros86904
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:07:00.196103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.18
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.1125638
Coefficient of variation (CV)3.4589838
Kurtosis35.773236
Mean0.03254245
Median Absolute Deviation (MAD)0
Skewness5.5481745
Sum4099.47
Variance0.01267061
MonotonicityNot monotonic
2023-04-16T00:07:00.528201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86904
69.0%
0.02 7952
 
6.3%
0.01 7146
 
5.7%
0.03 4723
 
3.7%
0.04 4518
 
3.6%
0.05 3048
 
2.4%
0.5 1550
 
1.2%
0.06 1330
 
1.1%
0.07 1036
 
0.8%
0.25 951
 
0.8%
Other values (65) 6815
 
5.4%
ValueCountFrequency (%)
0 86904
69.0%
0.01 7146
 
5.7%
0.02 7952
 
6.3%
0.03 4723
 
3.7%
0.04 4518
 
3.6%
0.05 3048
 
2.4%
0.06 1330
 
1.1%
0.07 1036
 
0.8%
0.08 488
 
0.4%
0.09 414
 
0.3%
ValueCountFrequency (%)
1 691
0.5%
0.97 2
 
< 0.1%
0.93 1
 
< 0.1%
0.88 1
 
< 0.1%
0.86 2
 
< 0.1%
0.83 2
 
< 0.1%
0.8 4
 
< 0.1%
0.78 1
 
< 0.1%
0.75 17
 
< 0.1%
0.73 2
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28445246
Minimum0
Maximum1
Zeros81386
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:07:00.846960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44478405
Coefficient of variation (CV)1.5636499
Kurtosis-1.0469956
Mean0.28445246
Median Absolute Deviation (MAD)0
Skewness0.96595232
Sum35833.33
Variance0.19783285
MonotonicityNot monotonic
2023-04-16T00:07:01.198076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81386
64.6%
1 33562
26.6%
0.01 3345
 
2.7%
0.02 1158
 
0.9%
0.03 711
 
0.6%
0.09 419
 
0.3%
0.08 413
 
0.3%
0.04 372
 
0.3%
0.99 304
 
0.2%
0.05 298
 
0.2%
Other values (91) 4005
 
3.2%
ValueCountFrequency (%)
0 81386
64.6%
0.01 3345
 
2.7%
0.02 1158
 
0.9%
0.03 711
 
0.6%
0.04 372
 
0.3%
0.05 298
 
0.2%
0.06 174
 
0.1%
0.07 197
 
0.2%
0.08 413
 
0.3%
0.09 419
 
0.3%
ValueCountFrequency (%)
1 33562
26.6%
0.99 304
 
0.2%
0.98 169
 
0.1%
0.97 100
 
0.1%
0.96 102
 
0.1%
0.95 71
 
0.1%
0.94 87
 
0.1%
0.93 76
 
0.1%
0.92 53
 
< 0.1%
0.91 48
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27848452
Minimum0
Maximum1
Zeros85360
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:07:01.528271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44566912
Coefficient of variation (CV)1.6003372
Kurtosis-1.007992
Mean0.27848452
Median Absolute Deviation (MAD)0
Skewness0.99173363
Sum35081.53
Variance0.19862097
MonotonicityNot monotonic
2023-04-16T00:07:01.792207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85360
67.8%
1 34256
27.2%
0.01 3762
 
3.0%
0.02 640
 
0.5%
0.03 160
 
0.1%
0.04 111
 
0.1%
0.5 107
 
0.1%
0.05 76
 
0.1%
0.08 72
 
0.1%
0.07 71
 
0.1%
Other values (90) 1358
 
1.1%
ValueCountFrequency (%)
0 85360
67.8%
0.01 3762
 
3.0%
0.02 640
 
0.5%
0.03 160
 
0.1%
0.04 111
 
0.1%
0.05 76
 
0.1%
0.06 53
 
< 0.1%
0.07 71
 
0.1%
0.08 72
 
0.1%
0.09 57
 
< 0.1%
ValueCountFrequency (%)
1 34256
27.2%
0.98 53
 
< 0.1%
0.97 56
 
< 0.1%
0.96 44
 
< 0.1%
0.95 26
 
< 0.1%
0.94 22
 
< 0.1%
0.93 20
 
< 0.1%
0.92 26
 
< 0.1%
0.91 20
 
< 0.1%
0.9 15
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11883181
Minimum0
Maximum1
Zeros103178
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:07:02.151313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30655746
Coefficient of variation (CV)2.5797592
Kurtosis3.6927487
Mean0.11883181
Median Absolute Deviation (MAD)0
Skewness2.3474458
Sum14969.6
Variance0.093977475
MonotonicityNot monotonic
2023-04-16T00:07:02.503177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103178
81.9%
1 10298
 
8.2%
0.01 1800
 
1.4%
0.02 1222
 
1.0%
0.03 497
 
0.4%
0.05 409
 
0.3%
0.04 397
 
0.3%
0.91 267
 
0.2%
0.92 257
 
0.2%
0.89 244
 
0.2%
Other values (91) 7404
 
5.9%
ValueCountFrequency (%)
0 103178
81.9%
0.01 1800
 
1.4%
0.02 1222
 
1.0%
0.03 497
 
0.4%
0.04 397
 
0.3%
0.05 409
 
0.3%
0.06 220
 
0.2%
0.07 164
 
0.1%
0.08 147
 
0.1%
0.09 104
 
0.1%
ValueCountFrequency (%)
1 10298
8.2%
0.99 52
 
< 0.1%
0.98 68
 
0.1%
0.97 106
 
0.1%
0.96 168
 
0.1%
0.95 123
 
0.1%
0.94 135
 
0.1%
0.93 111
 
0.1%
0.92 257
 
0.2%
0.91 267
 
0.2%

dst_host_srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12023989
Minimum0
Maximum1
Zeros106616
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:07:02.807463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31945939
Coefficient of variation (CV)2.6568503
Kurtosis3.5206619
Mean0.12023989
Median Absolute Deviation (MAD)0
Skewness2.3379263
Sum15146.98
Variance0.1020543
MonotonicityNot monotonic
2023-04-16T00:07:03.161482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106616
84.6%
1 13231
 
10.5%
0.01 1390
 
1.1%
0.02 580
 
0.5%
0.03 352
 
0.3%
0.05 351
 
0.3%
0.04 344
 
0.3%
0.98 189
 
0.2%
0.99 188
 
0.1%
0.06 185
 
0.1%
Other values (91) 2547
 
2.0%
ValueCountFrequency (%)
0 106616
84.6%
0.01 1390
 
1.1%
0.02 580
 
0.5%
0.03 352
 
0.3%
0.04 344
 
0.3%
0.05 351
 
0.3%
0.06 185
 
0.1%
0.07 97
 
0.1%
0.08 66
 
0.1%
0.09 39
 
< 0.1%
ValueCountFrequency (%)
1 13231
10.5%
0.99 188
 
0.1%
0.98 189
 
0.2%
0.97 103
 
0.1%
0.96 78
 
0.1%
0.95 73
 
0.1%
0.94 75
 
0.1%
0.93 50
 
< 0.1%
0.92 38
 
< 0.1%
0.91 51
 
< 0.1%

attack_category
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
normal
67343 
neptune
41214 
satan
 
3633
ipsweep
 
3599
portsweep
 
2931
Other values (18)
7253 

Length

Max length15
Median length6
Mean length6.3869877
Min length3

Characters and Unicode

Total characters804588
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rowneptune
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 67343
53.5%
neptune 41214
32.7%
satan 3633
 
2.9%
ipsweep 3599
 
2.9%
portsweep 2931
 
2.3%
smurf 2646
 
2.1%
nmap 1493
 
1.2%
back 956
 
0.8%
teardrop 892
 
0.7%
warezclient 890
 
0.7%
Other values (13) 376
 
0.3%

Length

2023-04-16T00:07:03.480252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal 67343
53.5%
neptune 41214
32.7%
satan 3633
 
2.9%
ipsweep 3599
 
2.9%
portsweep 2931
 
2.3%
smurf 2646
 
2.1%
nmap 1493
 
1.2%
back 956
 
0.8%
teardrop 892
 
0.7%
warezclient 890
 
0.7%
Other values (13) 376
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 155805
19.4%
e 98333
12.2%
a 78971
9.8%
r 75715
9.4%
m 71529
8.9%
o 71472
8.9%
l 68309
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (14) 33924
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 804497
> 99.9%
Connector Punctuation 91
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 155805
19.4%
e 98333
12.2%
a 78971
9.8%
r 75715
9.4%
m 71529
8.9%
o 71472
8.9%
l 68309
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (13) 33833
 
4.2%
Connector Punctuation
ValueCountFrequency (%)
_ 91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 804497
> 99.9%
Common 91
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 155805
19.4%
e 98333
12.2%
a 78971
9.8%
r 75715
9.4%
m 71529
8.9%
o 71472
8.9%
l 68309
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (13) 33833
 
4.2%
Common
ValueCountFrequency (%)
_ 91
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 155805
19.4%
e 98333
12.2%
a 78971
9.8%
r 75715
9.4%
m 71529
8.9%
o 71472
8.9%
l 68309
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (14) 33924
 
4.2%

occurance
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.50406
Minimum0
Maximum21
Zeros66
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-04-16T00:07:03.752918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q118
median20
Q321
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2915029
Coefficient of variation (CV)0.11748851
Kurtosis13.369272
Mean19.50406
Median Absolute Deviation (MAD)1
Skewness-2.8967749
Sum2456985
Variance5.2509857
MonotonicityNot monotonic
2023-04-16T00:07:04.033359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 62557
49.7%
18 20667
 
16.4%
20 19339
 
15.4%
19 10284
 
8.2%
15 3990
 
3.2%
17 3074
 
2.4%
16 2393
 
1.9%
12 729
 
0.6%
14 674
 
0.5%
11 641
 
0.5%
Other values (12) 1625
 
1.3%
ValueCountFrequency (%)
0 66
 
0.1%
1 62
 
< 0.1%
2 54
 
< 0.1%
3 65
 
0.1%
4 79
0.1%
5 81
0.1%
6 96
0.1%
7 118
0.1%
8 106
0.1%
9 194
0.2%
ValueCountFrequency (%)
21 62557
49.7%
20 19339
 
15.4%
19 10284
 
8.2%
18 20667
 
16.4%
17 3074
 
2.4%
16 2393
 
1.9%
15 3990
 
3.2%
14 674
 
0.5%
13 451
 
0.4%
12 729
 
0.6%

Interactions

2023-04-16T00:06:20.107157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:15.928279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:22.391629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:29.456430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-16T00:02:46.090896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:53.302114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:00.870700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:08.643940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:16.563297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:25.949033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-16T00:05:55.733756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-16T00:03:17.151872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-16T00:03:15.159577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:24.243004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:35.552473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:45.146023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:53.976267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:02.609411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:11.705346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:20.979001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:29.984016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:39.755649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:49.412398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:58.054847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:07.024508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:16.303039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:25.240079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:34.454898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:43.131725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:54.160004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:03.564760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:12.473698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:19.136813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:25.756753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:21.713944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:28.775702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:37.349110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:45.319207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:52.629046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:00.124254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:07.834031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:15.525718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:24.596711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:35.878865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:45.441421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:54.268009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:02.902595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:12.055726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:21.296331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:30.275164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:40.074246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:49.690418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:58.354712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:07.329053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:16.588686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:25.569948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:34.744647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:43.419293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:54.501813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:03.869055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:12.715160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:19.386037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:26.161833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:21.939830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:29.018938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:37.659825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:45.585120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:52.830612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:00.368990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:08.086055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:15.890608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:25.078148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:36.148301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:45.750216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:54.595439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:03.255133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:12.344531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:21.544103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:30.578086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:40.448218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:50.019760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:58.680951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:07.634064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:16.947708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:25.930430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:35.102794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:44.712560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:54.824512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:04.180323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:12.957106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:19.621537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:26.596673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:22.172217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:29.268500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:37.906297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:45.837686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:02:53.070869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:00.619220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:08.358984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:16.251735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:25.534652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:36.478270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:46.044216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:03:54.910729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:03.621517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:12.638221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:21.821210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:30.868744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:40.767714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:50.308966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:04:58.997985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:08.031893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:17.240455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:26.209449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:35.412599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:45.122026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:05:55.184670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:04.516185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:13.183090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T00:06:19.873595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-16T00:07:04.351035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
durationsrc_bytesdst_byteshotnum_failed_loginsnum_compromisednum_rootnum_file_creationsnum_access_filescountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateoccuranceprotocol_typeserviceflaglandwrong_fragmenturgentlogged_inroot_shellsu_attemptednum_shellsis_host_loginis_guest_loginattack_category
duration1.0000.2260.1490.2290.0580.0800.0420.0870.048-0.324-0.319-0.185-0.1820.0510.0460.169-0.1400.016-0.067-0.156-0.1400.1960.182-0.027-0.156-0.1530.0650.069-0.0080.0820.1600.1850.0000.0000.0090.0650.1630.1820.0000.0000.0190.164
src_bytes0.2261.0000.7000.2040.0130.1550.0810.0730.063-0.524-0.053-0.674-0.653-0.363-0.3440.753-0.7050.289-0.4050.6210.617-0.5250.3770.344-0.624-0.609-0.229-0.2590.2800.0000.0070.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.013
dst_bytes0.1490.7001.0000.2000.0250.171-0.0130.0400.068-0.440-0.017-0.536-0.510-0.300-0.2780.630-0.6080.310-0.3420.7080.667-0.6250.0590.327-0.519-0.483-0.219-0.1860.4600.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.018
hot0.2290.2040.2001.0000.0930.5150.0090.0530.002-0.153-0.143-0.086-0.083-0.0090.0120.107-0.104-0.028-0.0690.0050.063-0.060-0.002-0.066-0.064-0.0700.0950.095-0.1520.0260.2750.0260.0000.0000.0000.0990.0460.0120.0150.0000.8210.154
num_failed_logins0.0580.0130.0250.0931.0000.0350.0370.0630.003-0.038-0.040-0.018-0.0180.0310.0300.023-0.022-0.017-0.031-0.0190.007-0.0030.015-0.0180.0070.0080.0300.029-0.0490.0080.0790.0520.0000.0000.2110.0150.0440.1050.0000.0000.0150.313
num_compromised0.0800.1550.1710.5150.0351.0000.1650.1120.093-0.087-0.079-0.062-0.060-0.0020.0270.076-0.075-0.014-0.0470.0070.069-0.068-0.002-0.053-0.031-0.0280.1250.148-0.1530.0000.0310.0290.0000.0000.0860.0100.2430.2910.0000.0000.0000.000
num_root0.0420.081-0.0130.0090.0370.1651.0000.1110.101-0.080-0.080-0.043-0.041-0.025-0.0260.050-0.044-0.027-0.054-0.031-0.0210.0390.0610.035-0.032-0.023-0.011-0.013-0.0020.0000.0390.0290.0000.0000.0710.0130.2880.3440.0000.0000.0000.000
num_file_creations0.0870.0730.0400.0530.0630.1120.1111.0000.062-0.052-0.051-0.022-0.024-0.015-0.0150.031-0.029-0.008-0.021-0.021-0.0100.0190.0160.002-0.001-0.0070.0010.002-0.0160.0000.0540.0330.0000.0000.0860.0290.1110.1640.0760.0000.0040.022
num_access_files0.0480.0630.0680.0020.0030.0930.1010.0621.000-0.054-0.044-0.032-0.030-0.019-0.0080.040-0.0390.009-0.0000.0120.0110.002-0.015-0.012-0.023-0.023-0.010-0.0070.0210.0160.0400.0150.0000.0000.0170.0670.4070.4660.0260.0000.0000.052
count-0.324-0.524-0.440-0.153-0.038-0.087-0.080-0.052-0.0541.0000.5190.5780.5420.0710.065-0.7200.617-0.3250.620-0.325-0.4290.362-0.546-0.5310.5360.5050.0220.024-0.1530.2810.3820.2540.0060.1100.0000.6490.0280.0120.0070.0000.0790.361
srv_count-0.319-0.053-0.017-0.143-0.040-0.079-0.080-0.051-0.0440.5191.0000.0730.108-0.208-0.2080.032-0.0390.2350.2180.3000.257-0.241-0.197-0.1220.0460.080-0.213-0.220-0.0530.5470.3600.0830.0000.2680.0000.2290.0070.0000.0000.0000.0290.301
serror_rate-0.185-0.674-0.536-0.086-0.018-0.062-0.043-0.022-0.0320.5780.0731.0000.973-0.174-0.179-0.7550.674-0.3250.430-0.523-0.5730.485-0.485-0.3890.9360.922-0.227-0.206-0.1620.2170.2460.3910.0230.1380.0000.4940.0170.0000.0030.0000.0610.311
srv_serror_rate-0.182-0.653-0.510-0.083-0.018-0.060-0.041-0.024-0.0300.5420.1080.9731.000-0.222-0.237-0.7060.625-0.3050.411-0.479-0.5280.439-0.471-0.3690.9190.942-0.276-0.259-0.1380.2150.2370.3770.0210.0410.0000.4960.0160.0000.0020.0000.0600.310
rerror_rate0.051-0.363-0.300-0.0090.031-0.002-0.025-0.015-0.0190.071-0.208-0.174-0.2221.0000.966-0.2230.234-0.1490.083-0.312-0.2920.288-0.014-0.078-0.179-0.2240.8390.884-0.1500.1270.1470.3700.0240.0240.0000.2900.0120.0000.0000.0000.0350.250
srv_rerror_rate0.046-0.344-0.2780.0120.0300.027-0.026-0.015-0.0080.065-0.208-0.179-0.2370.9661.000-0.2100.213-0.1210.075-0.297-0.2750.273-0.020-0.075-0.187-0.2380.8310.894-0.1410.1280.1120.3610.0000.0240.0000.2930.0060.0000.0000.0000.0350.211
same_srv_rate0.1690.7530.6300.1070.0230.0760.0500.0310.040-0.7200.032-0.755-0.706-0.223-0.2101.000-0.9200.385-0.5410.6990.758-0.6510.5250.488-0.717-0.678-0.144-0.1570.1770.2110.3200.2960.0060.0450.0000.6060.0270.0110.0070.0000.0720.321
diff_srv_rate-0.140-0.705-0.608-0.104-0.022-0.075-0.044-0.029-0.0390.617-0.0390.6740.6250.2340.213-0.9201.000-0.3760.526-0.668-0.7270.646-0.443-0.4820.6450.5970.1560.160-0.2110.1190.1600.1460.0000.0160.0000.1740.0000.0000.0000.0000.0170.224
srv_diff_host_rate0.0160.2890.310-0.028-0.017-0.014-0.027-0.0080.009-0.3250.235-0.325-0.305-0.149-0.1210.385-0.3761.000-0.3110.3960.442-0.4040.1600.342-0.327-0.301-0.136-0.1250.1380.2780.2580.1090.0410.1070.0000.3350.0110.0000.0000.0000.0400.212
dst_host_count-0.067-0.405-0.342-0.069-0.031-0.047-0.054-0.021-0.0000.6200.2180.4300.4110.0830.075-0.5410.526-0.3111.000-0.350-0.5320.435-0.693-0.8380.4230.3800.0520.030-0.1270.2290.2430.1610.0330.0520.0050.4640.0300.0110.0150.0000.0740.232
dst_host_srv_count-0.1560.6210.7080.005-0.0190.007-0.031-0.0210.012-0.3250.300-0.523-0.479-0.312-0.2970.699-0.6680.396-0.3501.0000.919-0.8410.1520.448-0.528-0.469-0.265-0.2490.3820.2530.4050.2520.0140.1190.0000.6490.0080.0160.0220.0000.1630.296
dst_host_same_srv_rate-0.1400.6170.6670.0630.0070.069-0.021-0.0100.011-0.4290.257-0.573-0.528-0.292-0.2750.758-0.7270.442-0.5320.9191.000-0.8990.3050.539-0.582-0.515-0.247-0.2200.2710.2190.4150.2660.0100.1350.0040.6300.0270.0360.0250.0000.2410.298
dst_host_diff_srv_rate0.196-0.525-0.625-0.060-0.003-0.0680.0390.0190.0020.362-0.2410.4850.4390.2880.273-0.6510.646-0.4040.435-0.841-0.8991.000-0.213-0.4900.5050.4340.2680.224-0.2600.1730.2120.2180.0070.0750.0110.1760.0100.0160.0000.0270.0300.276
dst_host_same_src_port_rate0.1820.3770.059-0.0020.015-0.0020.0610.016-0.015-0.546-0.197-0.485-0.471-0.014-0.0200.525-0.4430.160-0.6930.1520.305-0.2131.0000.561-0.455-0.4500.039-0.008-0.1240.4360.2870.1510.0370.1490.0040.2120.0060.0000.0310.0000.0390.272
dst_host_srv_diff_host_rate-0.0270.3440.327-0.066-0.018-0.0530.0350.002-0.012-0.531-0.122-0.389-0.369-0.078-0.0750.488-0.4820.342-0.8380.4480.539-0.4900.5611.000-0.385-0.340-0.062-0.0380.2130.4390.2950.0890.1010.0540.1030.1440.0280.0200.0000.0000.0200.357
dst_host_serror_rate-0.156-0.624-0.519-0.0640.007-0.031-0.032-0.001-0.0230.5360.0460.9360.919-0.179-0.187-0.7170.645-0.3270.423-0.528-0.5820.505-0.455-0.3851.0000.919-0.195-0.206-0.1750.2130.2500.3430.0230.0790.0000.4970.0280.0380.0160.0680.0620.315
dst_host_srv_serror_rate-0.153-0.609-0.483-0.0700.008-0.028-0.023-0.007-0.0230.5050.0800.9220.942-0.224-0.238-0.6780.597-0.3010.380-0.469-0.5150.434-0.450-0.3400.9191.000-0.272-0.250-0.1160.2110.2550.3740.1010.0410.0000.4960.0680.0810.0530.0630.0610.299
dst_host_rerror_rate0.065-0.229-0.2190.0950.0300.125-0.0110.001-0.0100.022-0.213-0.227-0.2760.8390.831-0.1440.156-0.1360.052-0.265-0.2470.2680.039-0.062-0.195-0.2721.0000.880-0.1910.1240.1460.3270.0000.1520.0000.2780.0170.0090.0060.0000.0330.239
dst_host_srv_rerror_rate0.069-0.259-0.1860.0950.0290.148-0.0130.002-0.0070.024-0.220-0.206-0.2590.8840.894-0.1570.160-0.1250.030-0.249-0.2200.224-0.008-0.038-0.206-0.2500.8801.000-0.1350.1280.1500.3560.0000.0240.0000.2830.0790.0620.0000.0000.0510.182
occurance-0.0080.2800.460-0.152-0.049-0.153-0.002-0.0160.021-0.153-0.053-0.162-0.138-0.150-0.1410.177-0.2110.138-0.1270.3820.271-0.260-0.1240.213-0.175-0.116-0.191-0.1351.0000.3410.2410.1510.0780.1950.0390.3700.1190.0330.0610.0000.1880.378
protocol_type0.0820.0000.0000.0260.0080.0000.0000.0000.0160.2810.5470.2170.2150.1270.1280.2110.1190.2780.2290.2530.2190.1730.4360.4390.2130.2110.1240.1280.3411.0000.9230.2780.0050.1920.0000.3850.0170.0070.0050.0000.0460.664
service0.1600.0070.0000.2750.0790.0310.0390.0540.0400.3820.3600.2460.2370.1470.1120.3200.1600.2580.2430.4050.4150.2120.2870.2950.2500.2550.1460.1500.2410.9231.0000.2980.1110.2160.0250.8690.1610.1250.0480.0000.8200.355
flag0.1850.0900.0230.0260.0520.0290.0290.0330.0150.2540.0830.3910.3770.3700.3610.2960.1460.1090.1610.2520.2660.2180.1510.0890.3430.3740.3270.3560.1510.2780.2981.0000.0210.0540.0000.6530.0530.0510.0070.0000.0790.435
land0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0230.0210.0240.0000.0060.0000.0410.0330.0140.0100.0070.0370.1010.0230.1010.0000.0000.0780.0050.1110.0211.0000.0000.0000.0100.0000.0000.0000.0000.0000.848
wrong_fragment0.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.2680.1380.0410.0240.0240.0450.0160.1070.0520.1190.1350.0750.1490.0540.0790.0410.1520.0240.1950.1920.2160.0540.0001.0000.0000.0760.0000.0000.0000.0000.0080.984
urgent0.0090.0000.0000.0000.2110.0860.0710.0860.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0040.0110.0040.1030.0000.0000.0000.0000.0390.0000.0250.0000.0000.0001.0000.0070.1120.0980.0000.0000.0000.169
logged_in0.0650.0000.0000.0990.0150.0100.0130.0290.0670.6490.2290.4940.4960.2900.2930.6060.1740.3350.4640.6490.6300.1760.2120.1440.4970.4960.2780.2830.3700.3850.8690.6530.0100.0760.0071.0000.0450.0310.0240.0000.1200.733
root_shell0.1630.0000.0000.0460.0440.2430.2880.1110.4070.0280.0070.0170.0160.0120.0060.0270.0000.0110.0300.0080.0270.0100.0060.0280.0280.0680.0170.0790.1190.0170.1610.0530.0000.0000.1120.0451.0000.6100.1450.0000.0000.342
su_attempted0.1820.0000.0000.0120.1050.2910.3440.1640.4660.0120.0000.0000.0000.0000.0000.0110.0000.0000.0110.0160.0360.0160.0000.0200.0380.0810.0090.0620.0330.0070.1250.0510.0000.0000.0980.0310.6101.0000.0230.0000.0000.110
num_shells0.0000.0000.0000.0150.0000.0000.0000.0760.0260.0070.0000.0030.0020.0000.0000.0070.0000.0000.0150.0220.0250.0000.0310.0000.0160.0530.0060.0000.0610.0050.0480.0070.0000.0000.0000.0240.1450.0231.0000.0000.0000.319
is_host_login0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0680.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
is_guest_login0.0190.0000.0000.8210.0150.0000.0000.0040.0000.0790.0290.0610.0600.0350.0350.0720.0170.0400.0740.1630.2410.0300.0390.0200.0620.0610.0330.0510.1880.0460.8200.0790.0000.0080.0000.1200.0000.0000.0000.0001.0000.301
attack_category0.1640.0130.0180.1540.3130.0000.0000.0220.0520.3610.3010.3110.3100.2500.2110.3210.2240.2120.2320.2960.2980.2760.2720.3570.3150.2990.2390.1820.3780.6640.3550.4350.8480.9840.1690.7330.3420.1100.3190.0000.3011.000

Missing values

2023-04-16T00:06:27.500698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-16T00:06:30.591072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateattack_categoryoccurance
00tcpftp_dataSF49100000000000000000220.00.00.00.01.000.000.00150250.170.030.170.000.000.000.050.00normal20
10udpotherSF146000000000000000001310.00.00.00.00.080.150.0025510.000.600.880.000.000.000.000.00normal15
20tcpprivateS000000000000000000012361.01.00.00.00.050.070.00255260.100.050.000.001.001.000.000.00neptune19
30tcphttpSF23281530000010000000000550.20.20.00.01.000.000.00302551.000.000.030.040.030.010.000.01normal21
40tcphttpSF199420000001000000000030320.00.00.00.01.000.000.092552551.000.000.000.000.000.000.000.00normal21
50tcpprivateREJ000000000000000000121190.00.01.01.00.160.060.00255190.070.070.000.000.000.001.001.00neptune21
60tcpprivateS000000000000000000016691.01.00.00.00.050.060.0025590.040.050.000.001.001.000.000.00neptune21
70tcpprivateS0000000000000000000117161.01.00.00.00.140.060.00255150.060.070.000.001.001.000.000.00neptune21
80tcpremote_jobS0000000000000000000270231.01.00.00.00.090.050.00255230.090.050.000.001.001.000.000.00neptune21
90tcpprivateS000000000000000000013381.01.00.00.00.060.060.00255130.050.060.000.001.001.000.000.00neptune21
durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateattack_categoryoccurance
1259630tcphttpSF33416000000010000000000330.000.000.00.01.000.000.002552551.000.000.000.000.000.00.000.0normal21
1259640tcpprivateS000000000000000000012891.001.000.00.00.070.050.00255120.050.060.000.001.001.00.000.0neptune21
1259650tcpsmtpSF22333650000010000000000110.000.000.00.01.000.000.00121.000.001.001.000.000.00.000.0normal19
1259660tcpprivateS000000000000000000011331.001.000.00.00.030.070.00255130.050.070.000.001.001.00.000.0neptune21
1259670tcphttpSF35937500000100000000003110.330.090.00.01.000.000.1832551.000.000.330.040.330.00.000.0normal18
1259680tcpprivateS0000000000000000000184251.001.000.00.00.140.060.00255250.100.060.000.001.001.00.000.0neptune20
1259698udpprivateSF1051450000000000000000220.000.000.00.01.000.000.002552440.960.010.010.000.000.00.000.0normal21
1259700tcpsmtpSF22313840000010000000000110.000.000.00.01.000.000.00255300.120.060.000.000.720.00.010.0normal18
1259710tcpkloginS000000000000000000014481.001.000.00.00.060.050.0025580.030.050.000.001.001.00.000.0neptune20
1259720tcpftp_dataSF15100000010000000000110.000.000.00.01.000.000.00255770.300.030.300.000.000.00.000.0normal21